Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data |
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| Affiliation: | 1. Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada;2. Department of Geography, The Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada;1. Centre de Recherche Public – Gabriel Lippmann (CRP-GL), Environment and Agro-biotechnologies Department (EVA), 41, rue du Brill, L-4422 Belvaux, Luxembourg;2. Trier University, Department of Environmental Remote Sensing and Geoinformatics, D-54286 Trier, Germany;3. Julius Kühn-Institut (JKI), Institute for Crop and Soil Science, Bundesallee 50, D-38116 Braunschweig, Germany;1. Digital Mountain and Remote Sensing Application Center, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu 610041, China;2. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of CAS, Beijing 100875, China;3. Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China;4. School of Geography, Beijing Normal University, Beijing 100875, China;1. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;2. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;4. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China;5. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;1. Agriculture and Agri-Food Canada, Soils and Crops Research and Development Centre, 2560 Hochelaga Blvd., Quebec City, QC G1V 2J3, Canada;2. Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Centre, 960 Carling Ave., Ottawa, ON K1A 0C6, Canada;3. La Financière agricole du Québec, 1400 De la Rive-Sud Blvd., St-Romuald, QC G6W 8K7, Canada;1. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 10097, China;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;3. Key Laboratory for Information Technologies in Agriculture, The Ministry of Agriculture, Beijing 100097, China;4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;5. Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China;6. Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;7. College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China;1. College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China;2. Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada;3. Key Laboratory of Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling 712100, China |
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| Abstract: | A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAIL) alone (R2 = 0.77 and RMSE = 231 g m−2). Assimilation of GLAI derived from the fusion dataset (GLAIF) resulted in a R2 of 0.71 and RMSE of 193 g m−2 while assimilating the combination of GLAIL and GLAIF led to further improvements (R2 = 0.76 and RMSE = 176 g m−2). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited. |
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| Keywords: | Crop model Biomass Data fusion Data assimilation Leaf area index |
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